1. Technical Field
The present disclosure generally relates to systems and methods for retrieving time series data used to perform data analysis. More particularly, the present, disclosure relates to systems and methods for retrieving time series data pertaining to machine variables matching one or more specified criteria.
2. Background
Service departments or organizations that service electronic machines often use diagnostic information generated by such machines to perform fault detection and analysis. The diagnostic information may be collected using sensors or other recording mechanisms within the machine to generate various data, such as operating conditions and performance characteristics.
Fault detection and analysis is commonly performed using such diagnostic information. In most cases, fault analysis is performed either at the customer site by field engineers or at the service department if the customer brought or sent the machine for service. In either case, a service engineer examines the diagnostic information in an attempt to determine the cause of the fault.
One problem with performing fault analysis in this manner is that the service engineer only has access to a limited amount of information regarding the cause of the fault. For example, the service engineer might only have access to the latest diagnostic information that is stored on the machine. Even if time series data is present for the diagnostic information, the service engineer likely is only able to analyze a single time series of diagnostic information at a time.
Some machines have been provided with data network connections so that diagnostic information pertaining to a machine's internal state can be periodically transmitted to a data warehouse for storage. However, analyzing the data in the data warehouse to determine meaningful information can be difficult.
In addition, storing time series data in an organized way may be problematic. For example, because the number of variables for which data is stored for each machine could be quite large and the amount of time series data continuously grows, existing database tables may not be able to store all values in a single table in which, for example, each row of the database corresponds to a single time stamp and each column represents a particular code.
Moreover, current database structures do not adequately permit both point queries in which a search is performed to find values corresponding to a value in a different time period and range queries in which all values within a predefined time range are matched with a particular value.
Furthermore, comparisons of data across a plurality of time series based on determining a correlation between the time series cannot be performed using conventional database structures.
Systems and methods for enabling a user to find similarities between patterns for multiple machine variables and using such similarities to assist in fault analysis of a machine in real time would be desirable.